A measurement of opacity is employed in a standard used by the U.S. Environmental Protection Agency (USEPA) for visible emissions. Opacity is defined as the fraction (usually expressed as a percentage) of a light beam, which is in its passage through a smoke plume (or any other attenuating medium), and is removed from that beam by absorption and/or scattering. P. Lilienfeld et al, Passive Remote Smoke Plume Opacity Sensing: A Technique, Applied Optics, Vol. 20, No. 5, 800-806, 1981. It can be mathematically defined as 100% minus the percentage of the transmitted light radiance to the initial light radiance. The existing methods for monitoring opacity include Method 9 of the USEPA (USEPA Visual Determination of the Opacity of Emissions from Stationary Sources, 60 CFR, App. A. (7-1-92 Edition), 849-855, 1992), in-stack transmissometer (Conner, W. D. and H. B. McElhoe, Comparison of Opacity Measurements by Trained Observer and In-Stack Transmissometer, Journal of the Air Pollution Control Association, Vol. 32, No. 9, 943-946, 1982), Light Detection and Ranging (LIDAR) (60 CFR, App. A, Alternate Method 1—Determination of the Opacity of Emissions from Stationary Sources Remotely by LIDAR, 855-873, 1992) and the Optical Digital Environmental Compliance System (ODECS) (aka DOCS) (Stretch, J. P. and B. Pfaff, Summary Report for the Joint NASA, Space Dynamics Laboratory, SCIENTECH, Inc., Remote Sensing Project on an Optical Digital Environmental Compliance System (ODECS), 1999).
USEPA Method 9 requires a certified human observer to quantify plume opacity in the ambient environment immediately outside of the source. This method introduces human bias and involves extensive labor costs to train personnel and implement the method. 60 CFR (1992). USEPA Method 9 requires that an observer have an individual opacity error, di, of less than 15% and an average absolute opacity error, d, of less than 7.5% for all fifty black and white plumes evaluated during a particular test. Passing this test certifies the observer for six months. The individual opacity error, di, is the absolute error between an individual opacity value, 02,i, as measured by the in-stack transmissometer, and the observed opacity value, 01,i, by the human or digital camera as described by:di=|01,i−02,i|  (1)where subscript i represents each corresponding measurement and observation for all fifty tests. The average absolute opacity error, d, is defined as:
                              d          _                =                                                            ∑                                  i                  =                  1                                n                            ⁢                                                                d                  i                                                                      n                    =                                    1              n                        ·                                          ∑                                  i                  =                  1                                n                            ⁢                                                                                    0                                          1                      ,                      i                                                        -                                      0                                          2                      ,                      i                                                                                                                                              (        2        )            where n is the number of paired observations.
The in-stack transmissometer method quantifies plume opacity within the exhaust stack of the source. The system requires installation and maintenance for each source. A transmissometer may be purchased for $7,000-$12,000. Conner and McElhoe (1982).
LIDAR is used as a research instrument to quantify the opacity of the atmosphere and is very expensive, e.g., at least tens of thousands of dollars. Furthermore, since LIDAR measurements inherently yield positive error for plume transmittance, LIDAR underestimates the opacity of plumes. CFR (1992). Cook, C. S., et al., Remote Measurement of Smoke Plume Transmittance Using LIDAR, Applied Optics, Vol. 11, No. 8, 209-215, 1972. Additionally, transmissometers and LIDARs do not provide records, e.g., photographs that may be useful for demonstrations and legal actions.
Use of photography to quantify ambient visual range and plume opacity is a promising method to lower costs, improve accuracy and precision, reduce subjectivity, and provide a photograph as a record of the observation. Photographic techniques have been used in visibility studies during the past thirty years as described below. The fundamental relationship between visual range, contrast, and the scattering coefficient was initially derived more than eighty years ago. Koschmieder, H., Beitr. Phys. freien Atm. 12, 171-181, 1924 as cited by Hoffer, T. E. et al., The Science of the Total Environment 23, 293-304, 1982. Then, Barber and Larson developed a relationship between the visual range (defined as the distance at which a large black object just disappears from view) and a backscattering coefficient based on Koschmieder's theory. Barber and Larson, Appl. Opt., 24(21), 3223-3525, 1985.
Visual range of the vista of an ambient environment was quantified using the concept of inherent contrast and film densities, when using a 35 mm camera, black and white film or color film, and a digitizer. Hoffer et al. (1982). These photographs were used to calculate visual range for clear sky conditions. However, factors such as horizon brightness, cloudiness and shadows were not included in the calculations. First principles needed to simulate the effect of uniform haze photographically were described in the early 1980s, and a visibility model was developed based on those principles. Malm, W. C. et al., Journal of the Air Pollution Control Association, 33, 126-129, 1983. Visibility modeling was then tested experimentally in the late 1980s. Larson, S. M. et al., Environmental Science & Technology, 22, 629-637, 1988. Also, the atmospheric transmittance and path radiance were determined using two cameras taking pictures for the same scene at different distances (Richard et al., Environ. Sci. Technol., 23, 182-186, 1989) based on the equations described in Malm et al. (1983). Optical measurement of transmission and scattered radiance for a ground-level plume generated by a stationary jet engine was completed with the use of two multi-detector teleradiometers and a 6.1 by 6.1 m contrasting panel located 4.6 m behind the plume. Johnson, C. E. et al., Transactions of the Air & Waste Management Association; Mathai, C. V., Ed., 348-362, 1990.
More sophisticated aerosol and radiative transfer models were then developed to simulate visual air quality conditions in the mid-1990's. Molenar, J. V. et al., Atmospheric Environment, 28, 1055-1063, 1994. Digital photos were used to characterize visibility in the late 1990's and the results from digital photos were compared with LIDAR measurements. Xie, X. et al., Chinese Science Bulletin, 44, 1130-1134, 1999.
The initial use of digital photography to quantify visible plume opacity was described with the Digital Opacity Compliance System (DOCS). Stretch and Pfaff (1999). DOCS is also termed ODECS and is protected by U.S. Pat. No. 6,597,799, to Pfaff et al., Optical Digital Environment Compliance System, issued Jul. 22, 2003. The patent provides a method and apparatus for analyzing a digital photograph of an effluent to determine its opacity. A photograph of the effluent is analyzed at the pixel level. Each pixel corresponding to the effluent is identified and the color of each effluent pixel is analyzed to calculate the opacity of that effluent pixel. An opacity value for the entire effluent is extrapolated based on the opacity values of each pixel.
DOCS has used a specific digital camera with software that is installed in the camera. The camera is self-calibrating for clear-sky backgrounds only. To calculate the opacity of a plume, the operator selects an area in the photograph. The area must include a part of the plume to be determined for opacity as well as a portion of the clear-sky background. The performance of DOCS was evaluated at USEPA-approved smoke schools under clear-sky conditions. McFarland, M. J. et al., Journal of the Air & Waste Management Association, 53, 724-730, 2003. DOCS was also evaluated in overcast-sky conditions. McFarland, M. J. et al., Journal of the Air & Waste Management Association, 54, 296-306, 2004.
DOCS uses a digital camera to quantify plume opacity from a point source but works autonomously, i.e., self-calibrates, only during the day under clear-sky conditions. McFarland et al. (2003). DOCS may quantify plume opacity when the sky is overcast or the background is a building or a mountain. However, the operator needs to provide a color scale from which to estimate the opacity of a 100% opaque plume. Stretch et al. (1999). Clear-sky backgrounds are not typical, especially in low altitude and humid environments such as in the Midwestern U.S. and in marine environments. Also, human bias is introduced for all of those measurements in which a clear-sky background is not available since an operator must choose the color scale for a 100% opaque plume.
What is needed is a method employing an inexpensive digital camera that will yield objective quantitative opacity results for clear-sky, cloudy-sky, and nighttime conditions and provide for efficient storage of results for future use with no need for interpretation by an operator. In select embodiments of the present invention, the Digital Optical Method (DOM™) quantifies the ratio of radiance values by means of a camera response curve obtained using objective measures. The radiance ratios are then used to calculate plume opacity. The DOM™ quantifies the opacity of a subject, such as a plume from a smoke stack, from digital photos using a pre-designed algorithm and an inexpensive and readily available digital camera. Very little training is needed to implement the DOM™ and it yields consistent objective quantitative results, i.e., without need for human interpretation. Moreover, the DOM™ provides a permanent photographic record of the subject.